Increased moist heat stress risk across China under warming climate

Abstract

Heatwaves have afflicted human health, ecosystem, and socioeconomy and are expected to intensify under warming climate. However, few efforts have been directed to moist heat stress (MHS) considering relative humidity and wind speed, and moist heat stress risk (MHSR) considering exposure and vulnerability. Here we showed MHS and MHSR variations across China during 1998–2100 using China Meteorological Administration Land Data Assimilation System datasets, the 6th Coupled Model Intercomparison Project (CMIP6) merged datasets, Gross Domestic Product, population and leaf area index. We detected increased MHS across China under different Shared Socioeconomic Pathways (SSPs). Specifically, the historical MHS occurred mostly during mid-July to mid-August. We found increasing trends of 0.08%/year, 0.249%/year, and 0.669%/year in the MHS-affected areas under SSP126, SSP245, and SSP585, respectively. Furthermore, we observed the highest increasing rate of MHSR in Northwest and Southwest China, while the MHSR across Northeast and North China under SSP126 shifted from increasing to decreasing trends. Noteworthy is that the increasing trend of MHSR under SSP585 is 1.5–2.6 times larger than that under SSP245, especially in North and South China. This study highlights spatiotemporal evolutions of MHS and MHSR and mitigation to moisture heat stress in a warming climate.

Comparative Analysis of Three Near-Surface Air Temperature Reanalysis Datasets in Inner Mongolia Region

“…Hu et al studied the performance of five different resolution reanalysis temperature products in the permafrost region of the Qinghai Tibet Plateau, including CFSR, ERA-Interim, GLDASNOAH, MERRA, JRA-55, and CMFD, of which ERA-Interim has a better overall performance [54]. Sun et al compared the daily maximum temperature performance of CLDAS, ERA5, and GLDAS in the Chinese Mainland, and found that the high-resolution CLDAS is significantly better than the other two products [55]. From the above research, it can be seen that the performance of high-resolution temperature data in intergenerational reanalysis data is usually better than that of low-resolution data in the same year, and high-resolution reanalysis temperature data can better depict small and medium-sized fine features.…”

Quantitative Mechanisms of the Responses of Abrupt Seasonal Temperature Changes and Warming Hiatuses in China to Their Influencing Factors

“…Some researchers believe that these hiatuses are caused by natural external forcing, while others believe that they are triggered by the internal variability of the climate system [18]. The occurrence of hiatuses is partially attributed to La Niña-like cooling in the eastern tropical Pacific Ocean [19]. The weakening of the polar stratospheric vortex leads to hiatuses in Eurasia in winter [20,21].…”